Autorentext

Hamidreza Arasteh is an Assistant Professor in the Power Systems Operation and Planning Research Department at the Niroo Research Institute, Tehran, Iran, and a Research Assistant at the Center for Research on Microgrids (CROM), Huanjiang Laboratory, Zhuji, Shaoxing, Zhejiang, China. He specializes in energy management, smart grids, microgrids, and electricity markets, with numerous research contributions in energy management and the integration of data analytics into power system operations. Pierluigi Siano is a Professor and Scientific Director of the Smart Grids and Smart Cities Laboratory at the University of Salerno, Italy. A Senior Member of IEEE, his research focuses on demand response, distributed energy resources, and power system planning. He serves on editorial boards for several prestigious journals in the field. Niki Moslemi is Head of the Power Systems Operation and Planning Research Department at the Niroo Research Institute in Tehran, Iran. She brings decades of experience in power quality, load forecasting, system resiliency, and data-driven energy strategies. Her leadership and research span multiple high-impact projects within the energy sector. Josep M. Guerrero is with Zhejiang University, Hangzhou, Zhejiang, China, a Director of the Center for Research on Microgrids (CROM), Huanjiang Laboratory, Zhuji, Shaoxing, China, and a Distinguished Senior Researcher at the Department of Electrical Engineering, University of Valladolid, Spain. His research interests include various aspects of microgrids, including power electronics and distributed energy resources.

Klappentext

Presents a comprehensive guide to transforming power systems through data Data-Driven Energy Management and Tariff Optimization in Power Systems offers an authoritative examination of how data science is reshaping the energy landscape. As the electricity sector grapples with increasing complexity, this timely volume responds to a growing demand for adaptive strategies that enable accurate forecasting, intelligent tariff design, and optimized resource allocation, underpinned by advanced analytics and machine learning. Drawing on global expertise and real-world case studies, the book bridges the theoretical and practical dimensions of energy systems management, providing deep insight into how data collected from smart meters, SCADA systems, and IoT devices can be mined for predictive modeling, demand response, and peak load management. The book's accessible structure and didactic approach make it suitable for a wide readership, while its breadth of topics ensures relevance across the spectrum of energy challenges. Integrating rigorous analysis with application-oriented strategies, this book:

• Presents advanced techniques in machine learning, predictive modeling, and pattern recognition tailored to energy management and tariff design
• Provides accessible explanations of complex algorithms through a didactic and visual teaching style, including informative tables and illustrations
• Highlights tools for grid stability, demand forecasting, and peak load management using high-resolution energy data

• Addresses the integration of renewable energy sources into existing infrastructures through data-driven optimization Designed for a broad audience, Data-Driven Energy Management and Tariff Optimization in Power Systems is ideal for upper-level undergraduate and graduate courses in energy management, power systems analytics, and smart grids as part of electrical engineering or energy policy programs. It is also an essential reference for power system engineers, energy analysts, researchers, and policymakers involved in grid planning and optimization.

  Inhalt

  About the Editors xiii

  List of Contributors xv

  Preface xix

  1 Fundamentals of Power System Data and Analytics 1
  Pouya Ramezanzadeh, Mohsen Parsa Moghaddam, and Reza Zamani

  1.1 Introduction 1

  1.2 Background 2

  1.2.1 Concept, Opportunities, and Challenges of Present and Future Power Systems 2

  1.2.2 Transformation in the Power Industry 3

  1.2.3 Drivers and Barriers 6

  1.3 Data-rich Power Systems 6

  1.3.1 Data Sources and Types 8

  1.3.2 Data Structure 10

  1.4 Data Analytics in Power Systems 11

  1.4.1 What Is Data Analytics? 12

  1.4.2 Analytics Techniques 12

  1.5 Data Analytics-Based Decision-Making in Future Power Systems 13

  1.5.1 Decision Framework 15

  1.5.1.1 Uncertainty Issues 15

  1.5.1.2 Behavioral Analytics 15

  1.5.1.3 Policy Mechanisms 15

  1.5.2 Computational Aspects 16

  1.6 Conclusion 16

  1.7 Future Trends and Challenges 16

  References 17

  2 Advanced Predictive Modeling for Energy Consumption and Demand 21
  Seyed Mohsen Hashemi and Abbas Marini

  2.1 The Role of Load Forecasting in Power System Planning 21

  2.2 Need for Short-Term Demand Forecasting 22

  2.3 Components of Power Demand and Factors Affecting Demand Growth 22

  2.3.1 Electricity Demand from the Consumer Type Perspective 23

  2.3.2 Electricity Demand from the Supply Perspective 23

  2.4 Electricity Demand in Networks with High Renewable Energy Sources 24

  2.5 Machine Learning and Its Applications in Demand Forecast 25

  2.5.1 Application of Clustering in Load Forecasting 27

  2.6 The Impact of Macro-decisions on Long-term Load Forecasting 28

  2.6.1 Natural Gas as a Primary Energy Carrier for Heating Demand 29

  2.7 Conclusion 34

  References 35

  3 Demand Response and Customer-Centric Energy Management 39
  Alireza Mansoori, Mohsen Parsa Moghaddam, and Reza Zamani

  3.1 Introduction 39

  3.2 Background 39

  3.3 Future Power Systems Aspects, Trends, and Challenges 41

  3.4 Transforming to Customer-Centric Era 41

  3.4.1 Differences Between Customer-Centric DR Solution and OtherWays in the Future

  Power System 42

  3.4.2 Drivers and Enablers 42

  3.5 Customer-Centric Power System Structure 45

  3.5.1 Physical Layer 45

  3.5.1.1 Physical Resources 45

  3.5.1.2 Physical Constraints of the System 46

  3.5.2 Cyber-Social Layers 49

  3.5.2.1 Centralized Approach (Traditional) 50

  3.5.2.2 Decentralized Approach (Future) 50

  3.6 Conclusion and Future Trends 54

  References 57

  4 Applications of Data Mining in Industrial Tariff Design and Energy Management: Concepts and Practical Insights 61
  Hamidreza Arasteh, Niki Moslemi, Majid Miri Larimi, Pierluigi Siano, Sobhan Naderian, andJosep M. Guerrero

  4.1 Introduction 61

  4.1.1 Data Mining: Concepts, Procedures, and Tools 61

  4.1.2 Energy Management and the Role of Data Mining 65

  4.1.3 Aims and Scope 66

  4.2 Investigating Industrial Load Data: Analysis Through Various Indexes 67

  4.3 Classification of Industries 86

  4.4 Discussion and Conclusions 90

  References 92

  5 Data-Driven Tariff Design for Equitable Energy Distribution 95
  Salah Bahramara, Hamidreza Arasteh, Asrin Seyedzahedi, and Khabat Ghamari

  5.1 Introduction 95

  5.1.1 Literature Review and Contributions 96

  5.1.2 Chapter Organization 97

  5.2 Proposed Approach and Formulations 97

  5.3 Describing the Case Study 98

  5.4 Simulation Results 100

  5.5 Conclusions and Future Works 100

  References 105

  6 Applying Artificial Intelligence to Improve the Penetration of Renewable Energy in Power Systems 107
  Abbas Marini and Seyed Mohsen Hashemi

  6.1 Introduction 107

  6.2 Machine Learning Techniques 109

  6.2.1 Artificial Neural Network and Deep Neural Network 110

  6.2.2 Convolutional Neural Network 111

  6.2.3 Recurrent Neural Network 111

  6.2.4 Long Short-Term Memory 112

  6.3 General View of ML/DL Methods for RES Integration 112

  6.3.1 Data Preprocessing 114

  6.3.1.1 Normalization 115

  6.3.1.2 Wrong/Missing Values and Outliers 115

  6.3.1.3 Data Resolution 115

  6.3.1.4 Inactive Time Data 116

  6.3.1.5 Data Augmentation 116

  6.3.1.6 Correlation 116

  6.3.1.7 Data Clustering 116

  6.3.2 Deterministic/Probabilistic Forecasting Methods 116

  6.3.2.1 Deterministic Methods 116

  6…